5 papers
Improving Large Vision-Language Models' Understanding for Flow Field Data
Xiaomei Zhang, Hanyu Zheng, Xiangyu Zhu +4
Large Vision-Language Models (LVLMs) have shown impressive capabilities across a range of tasks that integrate visual and textual understanding, such as image captioning and visual…
GA-Field: Geometry-Aware Vehicle Aerodynamic Field Prediction
Zhenhua Zheng, Lu Zhang, Junhong Zou +4
Accurate aerodynamic field prediction is crucial for vehicle drag evaluation, but the computational cost of high-fidelity CFD hinders its use in iterative design workflows. While l…
UniField: Joint Multi-Domain Training for Universal Surface Pressure Modeling
Junhong Zou, Zhenxu Sun, Yueqing Wang +4
Accurate modeling of surface pressure fields around objects is fundamental to aerodynamic analysis and design. While neural networks have shown promise as efficient alternatives to…
AdaField: Generalizable Surface Pressure Modeling with Physics-Informed Pre-training and Flow-Conditioned Adaptation
Junhong Zou, Wei Qiu, Zhenxu Sun +3
The surface pressure field of transportation systems, including cars, trains, and aircraft, is critical for aerodynamic analysis and design. In recent years, deep neural networks h…
Top-Down Guidance for Learning Object-Centric Representations
Junhong Zou, Xiangyu Zhu, Zhaoxiang Zhang +1
Humans' innate ability to decompose scenes into objects allows for efficient understanding, predicting, and planning. In light of this, Object-Centric Learning (OCL) attempts to en…